Garuda - Garba Rujukan Digital

MAGNIFIKASI PERBAIKAN CITRA DIJITAL MULTI RESOLUSI DENGAN METODE GABUNGAN TAPIS LOLOS BAWAH DAN INTERPOLASI BILINEAR Cahyo Darujati; Syamsul Anam; Hasan Dwi Cahyono; Agustinus Bimo Gumelar
Jurnal Ilmiah Mikrotek Vol 1, No 2 (2014): FEBRUARI
Publisher : Universitas Trunojoyo Madura

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Dalam teknik pengolahan citra digital (digital image procesing), proses magnifikasi merupakan suatuproses yang bertujuan untuk memperbesar ukuran citra. Magnifikasi citra sangat erat kaitannya denganukuran penyimpanan yang tinggi dan dalam media jamak merupakan subyek yang valueable dalampengolahan citra. Magnifikasi juga merupakan proses pembesaran sesuatu hanya dalam penampilan, tidakdalam ukuran fisik. Beberapa penelitian sebelumnya, menggunakan teknik untuk memperbesar seluruhobyek dalam citra digital, tetapi diperlukan perbesaran pada obyek tertentu. Penelitian ini bertujuanmengimplementasikan algoritma untuk memagnifikasi citra digital pada citra dunia nyata yang tidakmemerlukan sejumlah besar masukan dari pengguna. Sebuah citra dunia nyata yang realistis akan menjadicitra yang bebas dari artefak seperti kabur (blurring), berbayang (shadowing) dan jaggies. Citra harusmencakup kontur halus dan juga transisi tepi yang cepat. Proses magnifikasi citra dilakukan dengan duatahap, yaitu proses pertama adalah proses pencuplikan citra digital, proses pencuplikan ini dilakukansecara manual, yang kemudian dilakukan proses filterisasi dan diperbesar dengan metode interpolasisecara bilinear. Keluarannya berupa nilai rata-rata keluaran dari kedua proses tersebut. Hasil penelitianmenunjukkan proses perbesaran citra diatas dua kali dengan metode gabungan menghasilkan nilai MeanSequare Error (MSE) lebih kecil dan nilai PSNR 73% lebih besar dibandingkan dengan metode nongabungan.

IMPLEMENTASI TEKNOLOGI SMART CAMERA BERBASIS INTERNET OF THING (IoT) PENDUKUNG PENGELOLA SEKOLAH DALAM MECEGAH PERUNDUNGAN (BULLYING) PESERTA DIDIK Haryono Setiadi; Dewi Wisnu Wardani; Ardhi Wijayanto; Hasan Dwi Cahyono
Prosiding Konferensi Nasional Pengabdian Kepada Masyarakat dan Corporate Social Responsibility (PKM-CSR) Vol 5 (2022): PERAN PERGURUAN TINGGI DAN DUNIA USAHA DALAM AKSELERASI PEMULIHAN DAMPAK PANDEMI
Publisher : Asosiasi Sinergi Pengabdi dan Pemberdaya Indonesia (ASPPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37695/pkmcsr.v5i0.1746

Pengabdian Masyarakat pada kegiatan ini dilatarbelakangi oleh maraknya perundungan siswa di sekolah. Hal ini perlu dilakukan pencegahan. Pengawasan guru dan entitas sekolah menjadi kritis untuk aktivitas pencegahan tersebut. Akan tetapi, guru dan entitas sekolah memiliki keterbatasan jangkauan untuk mengawasi seluruh area lingkungan sekolah. Dengan demikian, dukungan teknologi Smart Camera berbasis Internet of Thing (IoT) berpotensi mendukung aktivitas pengawasan lingkungan sekolah untuk mencegah terjadinya perundungan di sekolah. Pada kegiatan ini, teknologi tersebut diterapkan di Sekolah Menengah Pertama (SMP) Negeri 2 Surakarta. Penerapannya dilakukan dengan instalasi smart camera di ruang kelas dan beberapa area tersembunyi seperti lorong, tempat parkir, belakang kantin, dan lain-lain yang mana lokasi-lokasi tersebut berpotensi menjadi tempat perundungan siswa. Penerapan teknologi dibarengi dengan sosialisasi, pelatihan dan pendampingan kepada para guru dan entitasnya mengenai penggunaan dan perwatan teknologi tersebut. Teknologi ini telah berhasil diaplikasikan di SMP Negeri 2 Surakarta dan berpotensi untuk diterapkan di sekolah lainnya.

Enhancing Participatory Learning at SMP Negeri 2 Jaten Karanganyar through the Integration of Technology Cahyono, Hasan Dwi; Wardani, Dewi Wisnu; Setiadi, Haryono; Wijayanto, Ardhi; Doewes, Afrizal
Amalee: Indonesian Journal of Community Research and Engagement Vol 5 No 1 (2024): Amalee: Indonesian Journal of Community Research and Engagement
Publisher : LP2M INSURI Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37680/amalee.v5i1.4816

The development of knowledge and technology significantly impacts literacy skills, essential for academic growth and school adaptation. Technology literacy is crucial for awareness and academic support, but a lack of technological knowledge can hinder education. To address this, the Indonesian government introduced the belajar.id platform, integrating Google Suite for Education (GSuite) to aid academic activities during the pandemic. Challenges like limited teacher-student interaction persist, necessitating the encouragement of electronic media and diverse educational material availability. They aimed to bridge teaching gaps, enhance technological skills, and ensure effective knowledge sharing, using participatory rural appraisal (PRA). The team of Research Group Data Information Knowledge and Engineering (RG DIKE) at the Universitas Sebelas Maret (UNS) Surakarta conducted a study on technology literacy's importance for students in SMP Negeri 2 Jaten Karanganyar. It emphasized technology's role in disaster management and prevention, striving for a strategic approach to technology-based education. Training sessions were conducted on August 15 and October 26, 2023, focused on belajar.id, GSE, and OBS integration. Teachers played a key role in guiding and updating their GSE and OBS knowledge. In summary, these sessions aimed to equip teachers and students with vital GSE and OBS skills, enhancing education quality and learning outcomes.

PENINGKATAN KEAMANAN LINGKUNGAN DENGAN PENERAPAN CCTV DI DUKUH SRIMULYO Cahyono, Hasan Dwi; Wardani, Dewi Wisnu; Hendrasuryawan, Brilyan; Setiadi, Haryono; Doewes, Afrizal; Anggrainingsih, Rini; Wijayanto, Ardhi
MINDA BAHARU Vol 8, No 2 (2024): Minda Baharu
Publisher : Universitas Riau Kepulauan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33373/jmb.v8i2.7015

. Berdasarkan kegiatan Pengabdian kepada Masyarakat (PkM) yang sudah terlaksana, yakni penerapan closed-circuit television (CCTV) di Dukuh Srimulyo, Boyolali, didapatkan hasil bahwa dapat membantu menyelesaikan masalah mitra. Adapun permasalahan yang ditemukan adalah kurangnya pengawasan yang dapat memberikan rasa aman kepada warga. Hal ini terjadi akibat banyaknya jalur kendaraan yang dapat melintasi wilayah tersebut tetapi belum diterapkan adanya pengawasan secara real-time. PkM ini bertujuan untuk mengatasi masalah keterbatasan tersebut. Solusi yang ditawarkan kepada mitra adalah penerapan CCTV yang dapat merekam kejadian di titik yang penting. Pendampingan dilakukan dalam bentuk pelatihan penggunaan CCTV dan penyediaan fasilitas untuk pengawasan yang akan digunakan oleh mitra. Adapun hasil dari PkM ini adalah ditemukan bahwa para warga memberikan sambutan baik dengan diterapkannya CCTV ini pada kegiatan yang dilakukan berdasarkan umpan balik yang diberikan setelah kegiatan selesai. Selanjutnya CCTV yang terpasang pada titik penting berjumlah dua dan telah melalui proses penelaahan bersama dengan warga. Adapun dampak yang diperoleh secara nyata setelah PkM ini berakhir adalah sebagian besar peserta dapat melakukan pengawasan secara mandiri menggunakan aplikasi aplikasi CCTV yang telah terpasang

SENTIMENT ANALYSIS CLASSIFICATION IN WOMEN'S E-COMMERCE REVIEWS WITH MACHINE LEARNING APPROACH Afan Firdaus, Alfiki Diastama; Rahmawan, Rizki Dwi; Mahendra, Yuzzar Rizky; Cahyono, Hasan Dwi
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.2392

User reviews on e-commerce are one of the important elements in e-commerce. User reviews can help potential buyers make decisions based on the experiences and opinions of other people, for example women's e-commerce reviews. In providing positive, neutral or negative sentiment reviews, understanding customer perceptions is challenging. Classifying sentiment reviews will solve this problem, several classification techniques have been carried out, but there is still room for development in the use of simple machine learning techniques and sampling to overcome data class imbalance. Classification techniques used in this paper include Naive Bayes, SVM, and KNN. These algorithms will be compared to determine the most accurate model. Several preprocessing techniques are also carried out such to balance the dataset using ROS and SMOTE. It was obtained that the SVM method with ROS had the highest accuracy of around 0.94 for accuracy value, 0.93 for precision value, 0.94 for recall, and 0.92 for F1-score value. This research shows that the use of sampling techniques such as ROS and SMOTE can be effective in balancing imbalanced datasets, thereby improving model classification performance. These findings can be a reference for developing more efficient and accurate sentiment classification models, especially in the case of imbalanced data.

STOCK PREDICTION PERFORMANCE OPTIMIZATION: ENHANCING COVARIANCE MATRIX WITH KNN Saputra , Iskandar Abdul Azis; Sidiq, Muhammad Rais; Guritno, Sangaji Suryo; Cahyono, Hasan Dwi
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.2399

Stock price prediction is a fundamental yet complex challenge in quantitative finance. With the increasing availability of data and advancements in machine learning techniques, various models have been developed to capture intricate patterns in stock price movements. While complex neural network models such as Recurrent Neural Networks (RNNs), Graph Neural Networks (GNNs), and Transformers have shown potential in handling stock market data, they often face optimization difficulties and performance limitations, especially when data is scarce. This paper explores the use of simpler and more accessible prediction methods, specifically Linear Regression (LR) and K-Nearest Neighbors (KNN), alongside more advanced models like Temporal Spatial Transformer (TST) and a Multi-Layer Perceptron (MLP) model called Stockmixer. The NASDAQ dataset is utilized in this study, providing a comprehensive view of stock market dynamics with high variability. Results indicate that KNN, among the evaluated models, exhibits superior and more stable performance in predicting validation data compared to MLP. KNN achieved a low Mean Squared Error (MSE) at 100 epochs, and demonstrated positive Information Coefficient (IC) and Return Information Coefficient (RIC) values. Additionally, it showed high Precision at 10 (P@10) and Sharpe Ratio (SR), making it a robust choice for stock price prediction tasks. In contrast, MLP, despite its sophistication, revealed some weaknesses, particularly in the alignment between predictions and actual values. These findings offer valuable insights into the effectiveness of various models for stock price prediction and suggest that simpler models like KNN can provide competitive results compared to more complex models.

OPTIMIZATION OF STOCK PRICE PREDICTION WITH RIDGE REGRESSION AND HYPERPARAMETER SELECTIONS Marwa, Adeline Fellita; Setiyawan, Sitti Ayuningrum; Cahyani, Yonaka Titin Nur; Cahyono, Hasan Dwi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.2384

Stock price prediction is a topic that has garnered significant attention in the investment world and has been the subject of various studies. Despite the massive attention, predicting stock price movements using algorithms remains challenging as the algorithms must be agile and highly adaptive to movement trends. Recent studies using deep learning methods for stock price prediction show that deep learning methods have high reliability. However, their computational complexity limits widespread implementation. This study aims to predict Netflix stock prices using a linear regression model with ridge and hyperparameter optimisation. The research consists of three stages: data preprocessing, building a linear regression model with ridge, and predicting and visualizing results. The dataset used is historical Netflix stock price data from 2017 to 2022. In the preprocessing stage, the data was normalized using MinMaxScaler and split into training and test sets. A ridge regression model was built with hyperparameter alpha optimization using GridSearch. Predictions were compared to stock prices and evaluated using Root Mean Squared Error (RMSE). The ridge regression model with hyperparameter optimization performed best with an RMSE of 13.8082. Although the linear regression model demonstrated the fastest execution time of 0.7717 seconds, the ridge regression model with hyperparameter optimization provided an optimal balance between prediction accuracy and time efficiency.

A sentiment analysis on skewed product reviews: Ben & Jerry's ice cream Dewi, Nabilla Nurulita; Amalia Utami, Sekar Gesti; Adiar, Shalsabila Aura; Cahyono, Hasan Dwi
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp364-373

Sentiment analysis of product reviews offers valuable insights into consumer perspectives, which can inform product development and marketing strategies. Given the growing importance of user-generated content like product reviews, this study explored sentiment classification in online reviews of Ben & Jerry's ice cream. We designed and evaluated three machine learning algorithms for sentiment classification: Naïve Bayes (NB), logistic regression (LR), and support vector machine (SVM). The dataset exhibited a significant class imbalance, with substantially more positive than negative reviews. We employed two oversampling techniques: the synthetic minority oversampling technique (SMOTE) and the adaptive synthetic sampling approach (ADASYN). With the original skewed data, NB, LR, and SVM achieved accuracies of 91.90%, 93.77%, and 95.09%, respectively. While SMOTE did not improve performance in some scenarios, ADASYN yielded positive results and generally enhanced model reliability across all algorithms. Post-balancing with ADASYN, the sentiment distribution became less skewed, and accuracies shifted to 92.04% for NB, 94.96% for LR, and 95.23% for SVM. The combination of SVM and ADASYN demonstrated promising results, suggesting this approach may offer robust and efficient performance for binary sentiment classification, especially with imbalanced datasets.

Enhancing Participatory Learning at SMP Negeri 2 Jaten Karanganyar through the Integration of Technology Cahyono, Hasan Dwi; Wardani, Dewi Wisnu; Setiadi, Haryono; Wijayanto, Ardhi; Doewes, Afrizal
Amalee: Indonesian Journal of Community Research and Engagement Vol. 5 No. 1 (2024): Amalee: Indonesian Journal of Community Research and Engagement
Publisher : LP2M INSURI Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37680/amalee.v5i1.4816

The development of knowledge and technology significantly impacts literacy skills, essential for academic growth and school adaptation. Technology literacy is crucial for awareness and academic support, but a lack of technological knowledge can hinder education. To address this, the Indonesian government introduced the belajar.id platform, integrating Google Suite for Education (GSuite) to aid academic activities during the pandemic. Challenges like limited teacher-student interaction persist, necessitating the encouragement of electronic media and diverse educational material availability. They aimed to bridge teaching gaps, enhance technological skills, and ensure effective knowledge sharing, using participatory rural appraisal (PRA). The team of Research Group Data Information Knowledge and Engineering (RG DIKE) at the Universitas Sebelas Maret (UNS) Surakarta conducted a study on technology literacy's importance for students in SMP Negeri 2 Jaten Karanganyar. It emphasized technology's role in disaster management and prevention, striving for a strategic approach to technology-based education. Training sessions were conducted on August 15 and October 26, 2023, focused on belajar.id, GSE, and OBS integration. Teachers played a key role in guiding and updating their GSE and OBS knowledge. In summary, these sessions aimed to equip teachers and students with vital GSE and OBS skills, enhancing education quality and learning outcomes.

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